AI/ML Lifecycle

AI/ML Lifecycle with Roles and Examples

1. Business Understanding

Activities:

• Define the business problem
• Gather requirements
• Set objectives and success criteria

Main Roles:

• Business Analyst
• Domain Expert
• Product Manager
• Data Scientist

Example:

Predict customer churn, sales forecasting, fraud detection.

2. Data Acquisition

Activities:

• Data sourcing
• Data collection
• Data ingestion

Main Roles:

• Data Engineer

Example:

Collect data from APIs, databases, websites, sensors, CSV files.

3. Data Storage and Management

Activities:

• Store data
• Integrate multiple sources
• Data warehousing

Main Roles:

• Data Engineer
• Database Administrator (DBA)

Example:

Store customer data in MySQL, cloud storage, or data warehouses.

4. Data Preparation

Activities:

• Data cleaning
• Data transformation
• Data validation
• Data quality checks

Main Roles:

• Data Engineer
• Data Scientist

Example:

Remove missing values, remove duplicates, normalize and format data.

5. ETL/ELT and Pipeline Engineering

Activities:

• Extract data
• Transform data
• Load data
• Create automated pipelines

Main Roles:

• Data Engineer

Example:

Automate movement of data from APIs to warehouses.

6. Data Analysis and Understanding

Activities:

• Exploratory Data Analysis (EDA)
• Visualization
• Statistical analysis

Main Roles:

• Data Analyst
• Data Scientist

Example:

Find trends, correlations, and patterns.

7. Feature Engineering and Dataset Preparation

Activities:

• Create features
• Encode categorical variables
• Scale data
• Split datasets into train/test/validation

Main Roles:

• Data Scientist
• ML Engineer

Example:

Convert age into age groups, normalize values.

8. Modeling

Activities:

• Select algorithm
• Train model

Main Roles:

• Data Scientist

Example:

Train Linear Regression, Random Forest, CNN, etc.

9. Evaluation and Optimization

Activities:

• Evaluate model performance
• Hyperparameter tuning
• Optimize model

Main Roles:

• Data Scientist
• ML Engineer

Example:

Measure Accuracy, Precision, Recall, RMSE and use Grid Search.

10. Model Packaging and API Development

Activities:

• Save trained model
• Create APIs for prediction

Main Roles:

• ML Engineer
• Backend Developer

Example:

Build a prediction API using Flask or FastAPI.

11. Deployment and Automation

Activities:

• Deploy model
• Set up CI/CD pipeline

Main Roles:

• ML Engineer
• DevOps Engineer

Example:

Deploy model on cloud servers.

12. Monitoring and Continuous Improvement

Activities:

• Monitor performance
• Detect drift
• Retrain models
• Improve pipelines

Main Roles:

• MLOps Engineer
• ML Engineer

Example:

Monitor prediction quality and retrain model with new data.

Entire flow in one line:

Business Understanding
↓
Data Acquisition
↓
Data Storage & Management
↓
Data Preparation
↓
ETL/ELT Pipelines
↓
Data Analysis
↓
Feature Engineering
↓
Modeling
↓
Evaluation & Optimization
↓
API Creation
↓
Deployment
↓
Monitoring & Improvement

Different Roles in Machine Learning domain

1. Business Analyst (BA)

Overall Work

A Business Analyst connects the business side and the technical side.

They understand:

• What problem the company is facing
• What solution is needed
• What success looks like

They usually do less coding and more:

• Requirement gathering
• Communication
• Documentation
• Process analysis

Example

Company problem:

Customers are leaving the platform.

Business Analyst asks:

• Why are customers leaving?
• What data do we have?
• Can AI help predict churn?
• What should be the business goal?

Expertise

• Business understanding
• Requirement analysis
• Process mapping
• Communication
• Documentation

Skills

Technical Skills

• Excel
• SQL basics
• Power BI/Tableau
• Documentation tools

Non-Technical Skills

• Communication
• Presentation
• Critical thinking
• Stakeholder management

2. Data Analyst

Overall Work

A Data Analyst studies data and finds:

• Trends
• Patterns
• Insights
• Business answers

They answer:

"What is happening?"

Example

Questions:

• Which product sells most?
• Which city gives maximum profit?
• Why did sales decrease?

They create:

• Charts
• Dashboards
• Reports

Expertise

• Data visualization
• Reporting
• Statistical analysis
• KPI analysis

Skills

Technical Skills

• SQL
• Excel
• Power BI
• Tableau
• Python/R basics
• Statistics

Important Concepts

• EDA
• Correlation
• Trend analysis
• Dashboards

3. Data Engineer

Overall Work

A Data Engineer builds the systems that:

• Collect data
• Move data
• Store data
• Clean data
• Process data

They build the data infrastructure.

They answer:

"How do we reliably handle huge amounts of data?"

Example

Website → API → Kafka → Spark → Data Warehouse

They create:

• ETL pipelines
• Data lakes
• Warehouses
• Streaming systems

Expertise

• Databases
• Big data systems
• Distributed computing
• Data pipelines
• Cloud platforms

Skills

Technical Skills

• SQL (very strong)
• Python
• Spark
• Hadoop
• Kafka
• Airflow
• Cloud (AWS/GCP/Azure)

Core Concepts

• ETL/ELT
• Data Warehousing
• Data Modeling
• Distributed Systems

4. Data Scientist

Overall Work

A Data Scientist builds models that:

• Predict
• Classify
• Recommend
• Forecast
• Detect patterns

They answer:

"What will happen?"

Example

• Fraud detection
• Stock prediction
• Recommendation systems
• Medical diagnosis

Expertise

• Machine Learning
• Statistics
• Mathematics
• Data analysis
• AI algorithms

Skills

Technical Skills

• Python/R
• Scikit-learn
• TensorFlow/PyTorch
• SQL
• Statistics

Important Concepts

• Regression
• Classification
• Clustering
• Deep Learning
• Feature Engineering

5. ML Engineer (Machine Learning Engineer)

Overall Work

An ML Engineer takes the model from the Data Scientist and makes it usable in real applications.

They focus on:

• Scalability
• APIs
• Deployment
• Speed
• Reliability

They answer:

"How do we use the ML model in production?"

Example

Mobile App → API → ML Model → Prediction

Expertise

• Software engineering
• ML deployment
• APIs
• Optimization

Skills

Technical Skills

• Python
• FastAPI/Flask
• Docker
• Kubernetes
• Cloud deployment
• CI/CD

Important Concepts

• Model serving
• API creation
• Containerization
• Optimization

6. DevOps Engineer

Overall Work

DevOps Engineers handle:

• Infrastructure
• Automation
• Deployment
• Servers
• CI/CD pipelines

They ensure software runs smoothly.

Example

They automate:

Code Push → Testing → Deployment

Expertise

• System administration
• Automation
• Cloud infrastructure

Skills

Technical Skills

• Linux
• Docker
• Kubernetes
• Jenkins
• GitHub Actions
• AWS/Azure/GCP

7. MLOps Engineer

Overall Work

MLOps combines:

• Machine Learning
• DevOps
• Automation

They maintain ML systems after deployment.

They answer:

"Is the model still performing well?"

Example

They monitor:

• Data drift
• Accuracy drop
• Retraining
• Pipeline failures

Expertise

• ML lifecycle automation
• Monitoring systems
• Production ML

Skills

Technical Skills

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD
• Cloud ML services

Important Concepts

• Model monitoring
• Retraining pipelines
• Experiment tracking

8. Database Administrator (DBA)

Overall Work

DBAs manage databases:

• Security
• Performance
• Backup
• Recovery
• Permissions

Example

They ensure:

Database is fast, secure, and always available

Expertise

• Database optimization
• Query tuning
• Security

Skills

Technical Skills

• MySQL
• PostgreSQL
• Oracle
• MongoDB

Important Concepts

• Indexing
• Backup
• Replication
• Security

Relationship Between Roles

Business Analyst
    ↓
Data Engineer
    ↓
Data Analyst
    ↓
Data Scientist
    ↓
ML Engineer
    ↓
MLOps / DevOps